Theories have been proposed on which it has been hoped that systems could be developed to receive patterns, such as audio or visual patterns, and automatically develop a scheme for categorizing those patterns into a specific number of categories. Thereafter, such a system should recognize whether a pattern belongs in a previously established category. For example, a system might recognize twenty six categories, each corresponding to a different letter of the alphabet. During a learning process, the system might receive a series of input patterns which may differ even within each category. Elements of the patterns which are not required to categorize the patterns should be rejected as noise.
One theory of category learning was proposed by Stephen Grossberg and is described in "Some Psychophysiological and Pharmacological Correlates of a Developmental, Cognitive and Motivational Theory", Brain and Information: Event Related Potentials, Volume 425, Annals of the New York Academy of Sciences, 1984. Under that theory, an input pattern would be stored in short term memory and the pattern would be applied through adaptive filters to select a category. The selected category, retained in another short term memory, would generate an expected pattern corresponding to that category through a parallel set of adaptive filters. The correspondence between the expected pattern and the input pattern would determine whether the input pattern belonged in the initially selected category. If it did, the adaptive filters would be modified to reflect information provided by the input pattern. If the input pattern did not belong in the selected category, this system would prevent modification of the adaptive filters associated with the category and select a new category.
The present invention is based on development of the initial Grossberg theories into an operable machine architecture.